Data Architect – Snowflake/Databricks
Systango Technologies Ltd · IT Services & Consulting
- Indore, India
- On-site
- Posted today
- Data & AI
About the job
Experience: 10 to 15 years
About Systango
Systango is a technology company listed on the NSE, delivering innovative digital solutions across Generative AI, AI/ML, Data Engineering, Analytics, Blockchain/Web3, Cloud, and FinTech. We partner with global clients to build scalable, secure, and high-performance technology solutions.
Role Overview
We are looking for an experienced Data Architect with strong expertise in Snowflake/Databricks to design and lead the implementation of scalable, cloud-native data platforms. The ideal candidate will combine deep technical expertise in modern data architecture with hands-on experience in data engineering, cloud ecosystems, data governance, and enterprise data strategy.
You will work closely with engineering teams, business stakeholders, and client-facing teams to translate business requirements into robust data architectures that support analytics, reporting, and AI/ML use cases.
Key Responsibilities
Data Architecture: Define end-to-end data architecture, including data lakes, lakehouses, modern data warehouses, data marts, and enterprise data platforms.
Snowflake & Databricks: Design and optimize data solutions using Snowflake OR Databricks, selecting appropriate architectural patterns based on business and technical requirements.
Data Engineering: Architect scalable batch and streaming pipelines, ETL/ELT workflows with medallion architecture, data transformations using dbt, and orchestration frameworks.
Cloud Platforms: Design data solutions on AWS, Azure, or GCP, integrating cloud storage, compute, security, and networking services.
Data Modelling: Define conceptual, logical, and physical data models, including dimensional modelling, Data Vault 2.0, and normalized data structures.
Performance & Cost Optimization: Optimize query performance, compute utilization, storage, workload management, and overall platform costs.
Data Governance & Security: Establish standards for data quality, lineage, metadata management, access control, encryption, privacy, and regulatory compliance.
Architecture Standards: Define reusable architecture patterns, engineering best practices, coding standards, CI/CD processes, and data platform design principles.
Technical Leadership: Mentor data engineers and technical leads, conduct architecture reviews, and guide teams through complex technical decisions.
Stakeholder Collaboration: Work with product owners, business stakeholders, and clients to understand requirements and communicate architectural decisions effectively.
AI & Analytics Enablement: Build data foundations that support BI, advanced analytics, machine learning, and Generative AI applications, including RAG and vector search use cases where applicable.
Required Skills & Experience
10–15 years of experience in data engineering, data architecture, or enterprise data platforms.
Strong hands-on expertise in Snowflake/Databricks , including architecture, implementation, optimization, and production-scale deployments.
Excellent proficiency in SQL and Python, with experience in PySpark and distributed data processing.
Strong understanding of modern data architecture patterns, including lakehouse, data warehouse, data lake, and medallion architecture.
Proven experience designing enterprise-grade ETL/ELT pipelines and scalable data integration frameworks.
Experience with cloud platforms such as AWS, Microsoft Azure, or GCP.
Strong understanding of data modelling, dimensional modelling, Data Vault, and data warehouse design principles.
Must have strong hands-on experience with Kafka, real-time data streaming, event-driven architectures, and high-throughput distributed data processing.
Experience with orchestration tools such as Airflow, Azure Data Factory, or equivalent platforms.
Familiarity with data governance, data quality frameworks, lineage, metadata management, and enterprise security.
Experience with Git, CI/CD, infrastructure as code, and automated data deployment practices.
Demonstrated ability to lead architecture discussions, mentor engineers, and make sound technical decisions.
Proven experience building and mentoring Data Engineering teams, including junior engineers, through technical guidance, code reviews, and best-practice implementation.
Must hold at least one valid Snowflake or Databricks certification.
Must have hands-on experience designing and optimizing production data platforms processing petabytes of data.
Excellent communication, problem-solving, and stakeholder management skills.
Good to Have
Experience with dbt, Delta Lake, Unity Catalog, Snowflake Dynamic Tables, or Snowpark.
Knowledge of Kafka, Spark Structured Streaming, or other real-time data processing technologies.
Experience with data mesh, domain-driven data architecture, or multi-tenant data platforms.
Experience building data platforms for AI/ML, GenAI, feature engineering, and vector search.
Exposure to FinTech, payments, financial services, or other data-intensive industries.
What Success Looks Like
Designing scalable, secure, and cost-efficient data platforms aligned with business objectives.
Establishing architecture standards that improve data reliability, quality, and engineering productivity.
Enabling faster delivery of analytics, reporting, and AI-driven solutions.
Providing technical direction and raising the overall architecture and engineering maturity of the team.